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<div class="title">graph_inception_v3.cpp</div>  </div>
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<a href="graph__inception__v3_8cpp.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2017-2018 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> * of this software and associated documentation files (the &quot;Software&quot;), to</span></div><div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> * THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="graph_8h.xhtml">arm_compute/graph.h</a>&quot;</span></div><div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_toolchain_support_8h.xhtml">support/ToolchainSupport.h</a>&quot;</span></div><div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="_graph_utils_8h.xhtml">utils/GraphUtils.h</a>&quot;</span></div><div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="preprocessor">#include &quot;<a class="code" href="utils_2_utils_8h.xhtml">utils/Utils.h</a>&quot;</span></div><div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="preprocessor">#include &lt;cstdlib&gt;</span></div><div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="preprocessor">#include &lt;tuple&gt;</span></div><div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;</div><div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a>;</div><div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a>;</div><div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;<span class="keyword">using namespace </span><a class="code" href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a>;</div><div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="keyword">class </span>InceptionV3Example : <span class="keyword">public</span> <a class="code" href="classarm__compute_1_1utils_1_1_example.xhtml">Example</a></div><div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;{</div><div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="keyword">public</span>:</div><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;    <span class="keywordtype">void</span> do_setup(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)<span class="keyword"> override</span></div><div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;        std::string data_path; <span class="comment">/* Path to the trainable data */</span></div><div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;        std::string image;     <span class="comment">/* Image data */</span></div><div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;        std::string label;     <span class="comment">/* Label data */</span></div><div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;</div><div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;        <span class="comment">// Create a preprocessor object</span></div><div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;        std::unique_ptr&lt;IPreprocessor&gt; preprocessor = arm_compute::support::cpp14::make_unique&lt;TFPreproccessor&gt;();</div><div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;</div><div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;        <span class="comment">// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON</span></div><div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;        <span class="keyword">const</span> <span class="keywordtype">int</span>    target         = argc &gt; 1 ? std::strtol(argv[1], <span class="keyword">nullptr</span>, 10) : 0;</div><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;        <a class="code" href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">Target</a>       target_hint    = <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab6dc388200717b5fae17342af13f5e41">set_target_hint</a>(target);</div><div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;        <a class="code" href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">FastMathHint</a> fast_math_hint = FastMathHint::DISABLED;</div><div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;</div><div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        <span class="comment">// Parse arguments</span></div><div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;        <span class="keywordflow">if</span>(argc &lt; 2)</div><div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;        {</div><div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;            <span class="comment">// Print help</span></div><div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [target] [path_to_data] [image] [labels] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;        }</div><div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div><div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;        {</div><div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [image] [labels] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        }</div><div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 3)</div><div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;        {</div><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;            data_path = argv[2];</div><div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[2] &lt;&lt; <span class="stringliteral">&quot; [image] [labels] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No image provided: using random values\n\n&quot;</span>;</div><div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        }</div><div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 4)</div><div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        {</div><div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;            data_path = argv[2];</div><div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;            image     = argv[3];</div><div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[2] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[3] &lt;&lt; <span class="stringliteral">&quot; [labels] [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No text file with labels provided: skipping output accessor\n\n&quot;</span>;</div><div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;        }</div><div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;        <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 5)</div><div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;        {</div><div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;            data_path = argv[2];</div><div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;            image     = argv[3];</div><div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;            label     = argv[4];</div><div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[1] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[2] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[3] &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; argv[4] &lt;&lt; <span class="stringliteral">&quot; [fast_math_hint]\n\n&quot;</span>;</div><div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;            std::cout &lt;&lt; <span class="stringliteral">&quot;No fast math info provided: disabling fast math\n\n&quot;</span>;</div><div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        }</div><div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;        <span class="keywordflow">else</span></div><div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        {</div><div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;            data_path      = argv[2];</div><div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;            image          = argv[3];</div><div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;            label          = argv[4];</div><div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;            fast_math_hint = (std::strtol(argv[5], <span class="keyword">nullptr</span>, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED;</div><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        }</div><div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;</div><div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        graph &lt;&lt; target_hint</div><div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;              &lt;&lt; fast_math_hint</div><div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">InputLayer</a>(TensorDescriptor(TensorShape(299U, 299U, 3U, 1U), <a class="code" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">DataType::F32</a>),</div><div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;                            <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">get_input_accessor</a>(image, std::move(preprocessor), <span class="keyword">false</span>))</div><div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(3U, 3U, 32U,</div><div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;                                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy&quot;</span>),</div><div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;                                  std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(2, 2, 0, 0))</div><div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_1a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                                                              <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;                                         <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;                                                              <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;                                         <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;                                                                                             <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;                                         0.001f)</div><div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_1a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_1a_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(3U, 3U, 32U,</div><div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;                                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy&quot;</span>),</div><div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;                                  std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;                                                              <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;                                         <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;                                                              <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;                                         <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;                                                                                             <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;                                         0.001f)</div><div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2a_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;</div><div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; 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                                        <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;                                                                                             <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;                                         0.001f)</div><div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2b_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_2b_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, PadStrideInfo(2, 2, 0, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;MaxPool_3a_3x3/MaxPool&quot;</span>)</div><div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160; 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                                        <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;                                                                                             <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;                                         0.001f)</div><div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_3b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160; 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                                 std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>), PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_4a_3x3/convolution&quot;</span>)</div><div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;                                                              <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;                                         <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;                                                              <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;                                         <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f), <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;                                                                                             <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;                                         0.001f)</div><div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_4a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Conv2d_4a_3x3/Relu&quot;</span>)</div><div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">PoolingType::MAX</a>, 3, PadStrideInfo(2, 2, 0, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;MaxPool_5a_3x3/MaxPool&quot;</span>);</div><div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;        graph &lt;&lt; get_inception_node_A(data_path, <span class="stringliteral">&quot;Mixed_5b&quot;</span>, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),</div><div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;                                      32U)</div><div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_5b/concat&quot;</span>);</div><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;        graph &lt;&lt; get_inception_node_A(data_path, <span class="stringliteral">&quot;Mixed_5c&quot;</span>, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),</div><div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;                                      64U, <span class="keyword">true</span>)</div><div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_5c/concat&quot;</span>);</div><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        graph &lt;&lt; get_inception_node_A(data_path, <span class="stringliteral">&quot;Mixed_5d&quot;</span>, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),</div><div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;                                      64U)</div><div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_5d/concat&quot;</span>);</div><div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;</div><div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;        graph &lt;&lt; get_inception_node_B(data_path, <span class="stringliteral">&quot;Mixed_6a&quot;</span>, 384U, std::make_tuple(64U, 96U, 96U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6a/concat&quot;</span>);</div><div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;        graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6b&quot;</span>, 192U, std::make_tuple(128U, 128U, 192U),</div><div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;                                      std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)</div><div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6b/concat&quot;</span>);</div><div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6c&quot;</span>, 192U, std::make_tuple(160U, 160U, 192U),</div><div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)</div><div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6c/concat&quot;</span>);</div><div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6d&quot;</span>, 192U, std::make_tuple(160U, 160U, 192U),</div><div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;                                      std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)</div><div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6d/concat&quot;</span>);</div><div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;        graph &lt;&lt; get_inception_node_C(data_path, <span class="stringliteral">&quot;Mixed_6e&quot;</span>, 192U, std::make_tuple(192U, 192U, 192U),</div><div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;                                      std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)</div><div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_6e/concat&quot;</span>);</div><div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;</div><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;        graph &lt;&lt; get_inception_node_D(data_path, <span class="stringliteral">&quot;Mixed_7a&quot;</span>, std::make_tuple(192U, 320U),</div><div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;                                      std::make_tuple(192U, 192U, 192U, 192U))</div><div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_7a/concat&quot;</span>);</div><div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;</div><div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;        graph &lt;&lt; get_inception_node_E(data_path, <span class="stringliteral">&quot;Mixed_7b&quot;</span>, 320U, std::make_tuple(384U, 384U, 384U),</div><div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;                                      std::make_tuple(448U, 384U, 384U, 384U), 192U)</div><div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_7b/concat&quot;</span>);</div><div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;        graph &lt;&lt; get_inception_node_E(data_path, <span class="stringliteral">&quot;Mixed_7c&quot;</span>, 320U, std::make_tuple(384U, 384U, 384U),</div><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;                                      std::make_tuple(448U, 384U, 384U, 384U), 192U, <span class="keyword">true</span>)</div><div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Mixed_7c/concat&quot;</span>);</div><div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;</div><div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;        graph &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>, 8, PadStrideInfo(1, 1, 0, 0, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>))).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Logits/AvgPool_1a_8x8/AvgPool&quot;</span>)</div><div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(1U, 1U, 1001U, <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;                                                                      <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy&quot;</span>),</div><div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;                                  <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path,</div><div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;                                                       <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy&quot;</span>),</div><div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;                                  PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;              .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Logits/Conv2d_1c_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">ReshapeLayer</a>(TensorShape(1001U)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Predictions/Reshape&quot;</span>)</div><div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">SoftmaxLayer</a>().<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(<span class="stringliteral">&quot;Predictions/Softmax&quot;</span>)</div><div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;              &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">OutputLayer</a>(<a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">get_output_accessor</a>(label, 5));</div><div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;</div><div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        <span class="comment">// Finalize graph</span></div><div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        GraphConfig config;</div><div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        config.use_tuner = (target == 2);</div><div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;        graph.finalize(target_hint, config);</div><div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;    }</div><div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;</div><div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;    <span class="keywordtype">void</span> do_run()<span class="keyword"> override</span></div><div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;<span class="keyword">    </span>{</div><div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;        graph.run();</div><div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;    }</div><div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;</div><div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;    <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">Stream</a> graph{ 0, <span class="stringliteral">&quot;InceptionV3&quot;</span> };</div><div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;</div><div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;<span class="keyword">private</span>:</div><div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a> get_inception_node_A(<span class="keyword">const</span> std::string &amp;data_path, std::string &amp;&amp;param_path,</div><div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;                                     <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> a_filt,</div><div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;                                     std::tuple&lt;unsigned int, unsigned int&gt; b_filters,</div><div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;                                     std::tuple&lt;unsigned int, unsigned int, unsigned int&gt; c_filters,</div><div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;                                     <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> d_filt,</div><div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;                                     <span class="keywordtype">bool</span>         is_name_different = <span class="keyword">false</span>)</div><div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    {</div><div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        std::string total_path = <span class="stringliteral">&quot;/cnn_data/inceptionv3_model/&quot;</span> + param_path + <span class="stringliteral">&quot;_&quot;</span>;</div><div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;</div><div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        <span class="comment">// This is due to a naming issue in the tf model</span></div><div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        std::string conv_id0 = <span class="stringliteral">&quot;_0a_&quot;</span>;</div><div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        std::string conv_id1 = <span class="stringliteral">&quot;2d_0b_&quot;</span>;</div><div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <span class="keywordflow">if</span>(is_name_different)</div><div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        {</div><div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;            conv_id0 = <span class="stringliteral">&quot;_0b_&quot;</span>;</div><div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;            conv_id1 = <span class="stringliteral">&quot;_1_0c_&quot;</span>;</div><div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        }</div><div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;</div><div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_a(graph);</div><div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        i_a &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160; 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           &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160; 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               <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;                0.001f)</div><div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;</div><div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160; 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               <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;                0.001f)</div><div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;</div><div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160; 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           .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x7/Relu&quot;</span>)</div><div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160; 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               <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;                0.001f)</div><div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_7x1/Relu&quot;</span>)</div><div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160; 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           &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;                0.001f)</div><div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;</div><div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160; 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           .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_1a_3x3/Relu&quot;</span>);</div><div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;</div><div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160; 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               <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;                0.001f)</div><div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_0/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;</div><div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160; 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               <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;                0.001f)</div><div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0a_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;</div><div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160; 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            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;             &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d_0b_1x3/Relu&quot;</span>);</div><div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160;</div><div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;        <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_b2(static_cast&lt;IStream &amp;&gt;(i_b));</div><div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;        i_b2 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;                 1U, 3U, std::get&lt;2&gt;(b_filters),</div><div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_weights.npy&quot;</span>),</div><div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160;                 std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;                 PadStrideInfo(1, 1, 0, 1))</div><div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;             .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1/convolution&quot;</span>)</div><div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160;             &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_1_Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;                 0.001f)</div><div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;             .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160;             &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_1/Conv2d&quot;</span> + conv_id + <span class="stringliteral">&quot;3x1/Relu&quot;</span>);</div><div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;</div><div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160; 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           &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;                0.001f)</div><div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0a_1x1/Relu&quot;</span>)</div><div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160; 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            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;                 0.001f)</div><div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160;             .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;             &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0c_1x3/Relu&quot;</span>);</div><div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;</div><div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160;        <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_c2(static_cast&lt;IStream &amp;&gt;(i_c));</div><div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;        i_c2 &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;                 1U, 3U, std::get&lt;3&gt;(c_filters),</div><div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_weights.npy&quot;</span>),</div><div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;                 std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;                 PadStrideInfo(1, 1, 0, 1))</div><div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160;             .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_3x1/convolution&quot;</span>)</div><div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;             &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;                 <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160;                 0.001f)</div><div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;             .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160;             &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/Conv2d_0d_3x1/Relu&quot;</span>);</div><div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;</div><div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;        <span class="comment">// Merge i_c1 and i_c2</span></div><div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160;        i_c &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a>(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_c1), std::move(i_c2)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_2/concat&quot;</span>);</div><div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160;</div><div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;        <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">SubStream</a> i_d(graph);</div><div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;        i_d &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">PoolingLayer</a>(PoolingLayerInfo(<a class="code" href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">PoolingType::AVG</a>, 3, PadStrideInfo(1, 1, 1, 1, <a class="code" href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">DimensionRoundingType::CEIL</a>), <span class="keyword">true</span>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/AvgPool_0a_3x3/AvgPool&quot;</span>)</div><div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div><div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160;                1U, 1U, d_filt,</div><div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_weights.npy&quot;</span>),</div><div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;                std::unique_ptr&lt;arm_compute::graph::ITensorAccessor&gt;(<span class="keyword">nullptr</span>),</div><div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160;                PadStrideInfo(1, 1, 0, 0))</div><div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/convolution&quot;</span>)</div><div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">BatchNormalizationLayer</a>(</div><div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy&quot;</span>),</div><div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy&quot;</span>),</div><div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">get_random_accessor</a>(1.f, 1.f),</div><div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;                <a class="code" href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">get_weights_accessor</a>(data_path, total_path + <span class="stringliteral">&quot;Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy&quot;</span>),</div><div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;                0.001f)</div><div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;            .<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm&quot;</span>)</div><div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;            &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">ActivationLayer</a>(ActivationLayerInfo(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::ActivationFunction::RELU</a>)).<a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">set_name</a>(param_path + <span class="stringliteral">&quot;/Branch_3/Conv2d_0b_1x1/Relu&quot;</span>);</div><div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160;</div><div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;        <span class="keywordflow">return</span> <a class="code" href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">BranchLayer</a>(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));</div><div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;    }</div><div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160;};</div><div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;</div><div class="line"><a name="l00868"></a><span class="lineno"><a class="line" href="graph__inception__v3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">  868</a></span>&#160;<span class="keywordtype">int</span> <a class="code" href="graph__inception__v3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span> **argv)</div><div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;{</div><div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160;    <span class="keywordflow">return</span> arm_compute::utils::run_example&lt;InceptionV3Example&gt;(argc, argv);</div><div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;}</div><div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_branch_layer.xhtml">arm_compute::graph::frontend::BranchLayer</a></div><div class="ttdoc">Branch Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00435">Layers.h:435</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_pooling_layer.xhtml">arm_compute::graph::frontend::PoolingLayer</a></div><div class="ttdoc">Pooling Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00336">Layers.h:336</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab6dc388200717b5fae17342af13f5e41"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab6dc388200717b5fae17342af13f5e41">arm_compute::graph_utils::set_target_hint</a></div><div class="ttdeci">graph::Target set_target_hint(int target)</div><div class="ttdoc">Utility function to return the TargetHint. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00370">GraphUtils.h:370</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_sub_stream.xhtml">arm_compute::graph::frontend::SubStream</a></div><div class="ttdoc">Sub stream class. </div><div class="ttdef"><b>Definition:</b> <a href="_sub_stream_8h_source.xhtml#l00047">SubStream.h:47</a></div></div>
<div class="ttc" id="_toolchain_support_8h_xhtml"><div class="ttname"><a href="_toolchain_support_8h.xhtml">ToolchainSupport.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_reshape_layer.xhtml">arm_compute::graph::frontend::ReshapeLayer</a></div><div class="ttdoc">Reshape Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00360">Layers.h:360</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff"><div class="ttname"><a href="namespacearm__compute.xhtml#a1fece1bd804e64f39f602d1c3969849aa5bdce8e6d9dc3efbbd31e90a8a181dff">arm_compute::DimensionRoundingType::CEIL</a></div><div class="ttdoc">Ceil rounding. </div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml_a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">arm_compute::ActivationLayerInfo::ActivationFunction::RELU</a></div><div class="ttdoc">Rectifier (  ) </div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_aaf0c8eff756108c8bb23aecf51d44f79"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#aaf0c8eff756108c8bb23aecf51d44f79">arm_compute::graph_utils::get_output_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_output_accessor(const std::string &amp;labels_path, size_t top_n=5, std::ostream &amp;output_stream=std::cout)</div><div class="ttdoc">Generates appropriate output accessor according to the specified labels_path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00330">GraphUtils.h:330</a></div></div>
<div class="ttc" id="utils_2_utils_8h_xhtml"><div class="ttname"><a href="utils_2_utils_8h.xhtml">Utils.h</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda"><div class="ttname"><a href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::Format::F32</a></div><div class="ttdoc">1 channel, 1 F32 per channel </div></div>
<div class="ttc" id="graph__inception__v3_8cpp_xhtml_a3c04138a5bfe5d72780bb7e82a18e627"><div class="ttname"><a href="graph__inception__v3_8cpp.xhtml#a3c04138a5bfe5d72780bb7e82a18e627">main</a></div><div class="ttdeci">int main(int argc, char **argv)</div><div class="ttdoc">Main program for Inception V3. </div><div class="ttdef"><b>Definition:</b> <a href="graph__inception__v3_8cpp_source.xhtml#l00868">graph_inception_v3.cpp:868</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_ab7e905c7bfd2944e67bd069a3de3e7a2"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#ab7e905c7bfd2944e67bd069a3de3e7a2">arm_compute::graph_utils::get_random_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_random_accessor(PixelValue lower, PixelValue upper, const std::random_device::result_type seed=0)</div><div class="ttdoc">Generates appropriate random accessor. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00260">GraphUtils.h:260</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_input_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_input_layer.xhtml">arm_compute::graph::frontend::InputLayer</a></div><div class="ttdoc">Input Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00045">Layers.h:45</a></div></div>
<div class="ttc" id="_graph_utils_8h_xhtml"><div class="ttname"><a href="_graph_utils_8h.xhtml">GraphUtils.h</a></div></div>
<div class="ttc" id="graph_8h_xhtml"><div class="ttname"><a href="graph_8h.xhtml">graph.h</a></div></div>
<div class="ttc" id="classarm__compute_1_1utils_1_1_example_xhtml"><div class="ttname"><a href="classarm__compute_1_1utils_1_1_example.xhtml">arm_compute::utils::Example</a></div><div class="ttdoc">Abstract Example class. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8h_source.xhtml#l00062">Utils.h:62</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a9984cc47279cdb732b7b83caf0627de6"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a9984cc47279cdb732b7b83caf0627de6">arm_compute::graph_utils::get_input_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_input_accessor(const std::string &amp;ppm_path, std::unique_ptr&lt; IPreprocessor &gt; preprocessor=nullptr, bool bgr=true)</div><div class="ttdoc">Generates appropriate input accessor according to the specified ppm_path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00299">GraphUtils.h:299</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_activation_layer.xhtml">arm_compute::graph::frontend::ActivationLayer</a></div><div class="ttdoc">Activation Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00094">Layers.h:94</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a31488d29805a596498c0234ae392d35d"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a31488d29805a596498c0234ae392d35d">arm_compute::graph::Target</a></div><div class="ttdeci">Target</div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00084">Types.h:84</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_convolution_layer.xhtml">arm_compute::graph::frontend::ConvolutionLayer</a></div><div class="ttdoc">Convolution Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00158">Layers.h:158</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_ac85a46f3ebd3ab09f576a994ac2dce11"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#ac85a46f3ebd3ab09f576a994ac2dce11">arm_compute::graph::FastMathHint</a></div><div class="ttdeci">FastMathHint</div><div class="ttdoc">Enable or disable fast math for Convolution layer. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00118">Types.h:118</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml">arm_compute::utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_cast_8h_source.xhtml#l00031">Cast.h:31</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml">arm_compute::graph_utils</a></div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00041">GraphUtils.h:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_softmax_layer.xhtml">arm_compute::graph::frontend::SoftmaxLayer</a></div><div class="ttdoc">Softmax Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00411">Layers.h:411</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a"><div class="ttname"><a href="namespacearm__compute.xhtml#a9172da722f0a434e5cc07c0a3c115d93afcefd647d6a866603c627b11347c707a">arm_compute::PoolingType::AVG</a></div><div class="ttdoc">Average Pooling. </div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_output_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_output_layer.xhtml">arm_compute::graph::frontend::OutputLayer</a></div><div class="ttdoc">Output Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00070">Layers.h:70</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph__utils_xhtml_a30bee0b52a919bbcb1dc48b1b6546a16"><div class="ttname"><a href="namespacearm__compute_1_1graph__utils.xhtml#a30bee0b52a919bbcb1dc48b1b6546a16">arm_compute::graph_utils::get_weights_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; graph::ITensorAccessor &gt; get_weights_accessor(const std::string &amp;path, const std::string &amp;data_file, DataLayout file_layout=DataLayout::NCHW)</div><div class="ttdoc">Generates appropriate weights accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00275">GraphUtils.h:275</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_stream_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_stream.xhtml">arm_compute::graph::frontend::Stream</a></div><div class="ttdoc">Stream frontend class to construct simple graphs in a stream fashion. </div><div class="ttdef"><b>Definition:</b> <a href="_stream_8h_source.xhtml#l00045">Stream.h:45</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_1_1frontend_xhtml"><div class="ttname"><a href="namespacearm__compute_1_1graph_1_1frontend.xhtml">arm_compute::graph::frontend</a></div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00031">ILayer.h:31</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_batch_normalization_layer.xhtml">arm_compute::graph::frontend::BatchNormalizationLayer</a></div><div class="ttdoc">Batchnormalization Layer. </div><div class="ttdef"><b>Definition:</b> <a href="_layers_8h_source.xhtml#l00118">Layers.h:118</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5"><div class="ttname"><a href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::NonLinearFilterFunction::MAX</a></div><div class="ttdoc">Non linear dilate. </div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1frontend_1_1_i_layer_xhtml_af664a2598e05f8de28fb9f94e3902886"><div class="ttname"><a href="classarm__compute_1_1graph_1_1frontend_1_1_i_layer.xhtml#af664a2598e05f8de28fb9f94e3902886">arm_compute::graph::frontend::ILayer::set_name</a></div><div class="ttdeci">ILayer &amp; set_name(std::string name)</div><div class="ttdoc">Sets the name of the layer. </div><div class="ttdef"><b>Definition:</b> <a href="_i_layer_8h_source.xhtml#l00055">ILayer.h:55</a></div></div>
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